Usuário(a):Stefanostefenon88/Testes

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Stefano Frizzo Stefenon[editar | editar código-fonte]

Researcher in the field of artificial intelligence with a focus on applying solutions to electrical power systems. He currently is a researcher at Fondazione Bruno Kessler in Italy. His interests include artificial intelligence for fault identification in electrical power systems, deep learning, computer vision, and wavelet transform.

He received the B.E. and M.E. degrees in Electrical Engineering (Power Systems) from the Regional University of Blumenau, Brazil, in 2012 and 2015 respectively. In 2021 received his Ph.D. in Electrical Engineering from the State University of Santa Catarina, Brazil. During his doctoral period, he developed a research project in the field of deep learning applied to computer vision at the Faculty of Engineering and Applied Science, University of Regina, Canada.

His research initially took place at the High Voltage Laboratory of the Regional University of Blumenau (FURB). During his master's degree, he investigated faults in medium-voltage distribution networks exposed to natural and artificial contamination [1]. During his investigation, he used ultrasound equipment to classify insulators. After inspecting the electrical system, with insulators in various conditions, he carried out laboratory analysis under controlled conditions [2] [3] [4].

To improve the ability to identify faults in the electrical power grids, he began his doctorate where, in his first work he focused on applying machine learning techniques to power systems was focused on the classification of defects in distribution insulators [5]. This work relied on a combination of various techniques such as bottom-up segmentation, wavelet energy coefficient, principal component analysis, and particle swarm optimization associated with an ensemble extreme learning machine. This work had superior results to other classification models and motivated him to do more in-depth research on the subject.

Some of the topics presented in [5] have shown promise and have prompted specific research into their application. Focusing on the use of the wavelet transform to noise reduction in time series, the following works were explored by the author, such as: [6], [7], and [8]. Combining of the wavelet transform with forecasting models has been extended to collaborative work with other authors, such as the doctoral work of Nathielle Waldrigues Branco [9].

The exploration of noise attenuation models resulted in research into other techniques such as the Hodrick–Prescott filter [10], empirical wavelet transform [11], and Christiano–Fitzgerald random walk filter [12].

  1. ↑ Stefenon, S. F.; Oliveira, J. R.; Coelho, A. S.; Meyer, L. H. (maio de 2017). «Diagnostic of Insulators of Conventional Grid Through LabVIEW Analysis of FFT Signal Generated from Ultrasound Detector». IEEE Latin America Transactions (5): 884–889. ISSN 1548-0992. doi:10.1109/TLA.2017.7910202. Consultado em 26 de abril de 2024 
  2. ↑ Frizzo Stefenon, Stéfano; Silva, Marcelo Campos; Bertol, Douglas Wildgrube; Meyer, Luiz Henrique; Nied, Ademir (22 de novembro de 2019). «Fault diagnosis of insulators from ultrasound detection using neural networks». Journal of Intelligent & Fuzzy Systems (5): 6655–6664. doi:10.3233/JIFS-190013. Consultado em 26 de abril de 2024 
  3. ↑ Frizzo Stefenon, Stéfano; Zanetti Freire, Roberto; Henrique Meyer, Luiz; Picolotto Corso, Marcelo; Sartori, Andreza; Nied, Ademir; Rodrigues Klaar, Anne Carolina; Yow, Kin-Choong (20 de dezembro de 2020). «Fault detection in insulators based on ultrasonic signal processing using a hybrid deep learning technique». IET Science, Measurement & Technology (em inglês) (10): 953–961. ISSN 1751-8822. doi:10.1049/iet-smt.2020.0083. Consultado em 26 de abril de 2024 
  4. ↑ Stefenon, Stefano Frizzo; Bruns, Rafael; Sartori, Andreza; Meyer, Luiz Henrique; Ovejero, Raul Garcia; Leithardt, Valderi Reis Quietinho (2022). «Analysis of the Ultrasonic Signal in Polymeric Contaminated Insulators Through Ensemble Learning Methods». IEEE Access: 33980–33991. ISSN 2169-3536. doi:10.1109/ACCESS.2022.3161506. Consultado em 26 de abril de 2024 
  5. ↑ a b Stefenon, Stefano Frizzo; Grebogi, Rafael Bartnik; Freire, Roberto Zanetti; Nied, Ademir; Meyer, Luiz Henrique (junho de 2020). «Optimized Ensemble Extreme Learning Machine for Classification of Electrical Insulators Conditions». IEEE Transactions on Industrial Electronics (6): 5170–5178. ISSN 0278-0046. doi:10.1109/TIE.2019.2926044. Consultado em 26 de abril de 2024 
  6. ↑ Stefenon, Stéfano Frizzo; Ribeiro, Matheus Henrique Dal Molin; Nied, Ademir; Yow, Kin-Choong; Mariani, Viviana Cocco; Coelho, Leandro dos Santos; Seman, Laio Oriel (janeiro de 2022). «Time series forecasting using ensemble learning methods for emergency prevention in hydroelectric power plants with dam». Electric Power Systems Research (em inglês). 107584 páginas. doi:10.1016/j.epsr.2021.107584. Consultado em 26 de abril de 2024 
  7. ↑ Frizzo Stefenon, Stéfano; Zanetti Freire, Roberto; dos Santos Coelho, Leandro; Meyer, Luiz Henrique; Bartnik Grebogi, Rafael; Gouvêa Buratto, William; Nied, Ademir (19 de janeiro de 2020). «Electrical Insulator Fault Forecasting Based on a Wavelet Neuro-Fuzzy System». Energies (em inglês) (2). 484 páginas. ISSN 1996-1073. doi:10.3390/en13020484. Consultado em 26 de abril de 2024 
  8. ↑ Stefenon, Stefano Frizzo; Ribeiro, Matheus Henrique Dal Molin; Nied, Ademir; Mariani, Viviana Cocco; Coelho, Leandro Dos Santos; Leithardt, Valderi Reis Quietinho; Silva, Luis Augusto; Seman, Laio Oriel (2021). «Hybrid Wavelet Stacking Ensemble Model for Insulators Contamination Forecasting». IEEE Access: 66387–66397. ISSN 2169-3536. doi:10.1109/ACCESS.2021.3076410. Consultado em 26 de abril de 2024 
  9. ↑ Branco, Nathielle Waldrigues; Cavalca, Mariana Santos Matos; Stefenon, Stefano Frizzo; Leithardt, Valderi Reis Quietinho (30 de outubro de 2022). «Wavelet LSTM for Fault Forecasting in Electrical Power Grids». Sensors (em inglês) (21). 8323 páginas. ISSN 1424-8220. PMC PMC9659285Acessível livremente Verifique |pmc= (ajuda). PMID 36366021. doi:10.3390/s22218323. Consultado em 26 de abril de 2024 
  10. ↑ Seman, Laio Oriel; Stefenon, Stefano Frizzo; Mariani, Viviana Cocco; Coelho, Leandro dos Santos (outubro de 2023). «Ensemble learning methods using the Hodrick–Prescott filter for fault forecasting in insulators of the electrical power grids». International Journal of Electrical Power & Energy Systems (em inglês). 109269 páginas. doi:10.1016/j.ijepes.2023.109269. Consultado em 26 de abril de 2024 
  11. ↑ Klaar, Anne Carolina Rodrigues; Stefenon, Stefano Frizzo; Seman, Laio Oriel; Mariani, Viviana Cocco; Coelho, Leandro dos Santos (17 de março de 2023). «Optimized EWT-Seq2Seq-LSTM with Attention Mechanism to Insulators Fault Prediction». Sensors (em inglês) (6). 3202 páginas. ISSN 1424-8220. PMC PMC10051368Acessível livremente Verifique |pmc= (ajuda). PMID 36991913 Verifique |pmid= (ajuda). doi:10.3390/s23063202. Consultado em 26 de abril de 2024 
  12. ↑ Stefenon, Stefano Frizzo; Seman, Laio Oriel; Sopelsa Neto, Nemesio Fava; Meyer, Luiz Henrique; Mariani, Viviana Cocco; Coelho, Leandro dos Santos (3 de julho de 2023). «Group Method of Data Handling Using Christiano–Fitzgerald Random Walk Filter for Insulator Fault Prediction». Sensors (em inglês) (13). 6118 páginas. ISSN 1424-8220. PMC PMC10346365Acessível livremente Verifique |pmc= (ajuda). PMID 37447968 Verifique |pmid= (ajuda). doi:10.3390/s23136118. Consultado em 26 de abril de 2024 
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